Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
69 lines
2.2 KiB
69 lines
2.2 KiB
4 years ago
|
"""Test deprecation and future warnings.
|
||
|
|
|
||
|
|
"""
|
||
|
|
import numpy as np
|
||
|
|
from numpy.testing import assert_warns
|
||
|
|
from numpy.ma.testutils import assert_equal
|
||
|
|
from numpy.ma.core import MaskedArrayFutureWarning
|
||
|
|
|
||
|
|
class TestArgsort:
|
||
|
|
""" gh-8701 """
|
||
|
|
def _test_base(self, argsort, cls):
|
||
|
|
arr_0d = np.array(1).view(cls)
|
||
|
|
argsort(arr_0d)
|
||
|
|
|
||
|
|
arr_1d = np.array([1, 2, 3]).view(cls)
|
||
|
|
argsort(arr_1d)
|
||
|
|
|
||
|
|
# argsort has a bad default for >1d arrays
|
||
|
|
arr_2d = np.array([[1, 2], [3, 4]]).view(cls)
|
||
|
|
result = assert_warns(
|
||
|
|
np.ma.core.MaskedArrayFutureWarning, argsort, arr_2d)
|
||
|
|
assert_equal(result, argsort(arr_2d, axis=None))
|
||
|
|
|
||
|
|
# should be no warnings for explicitly specifying it
|
||
|
|
argsort(arr_2d, axis=None)
|
||
|
|
argsort(arr_2d, axis=-1)
|
||
|
|
|
||
|
|
def test_function_ndarray(self):
|
||
|
|
return self._test_base(np.ma.argsort, np.ndarray)
|
||
|
|
|
||
|
|
def test_function_maskedarray(self):
|
||
|
|
return self._test_base(np.ma.argsort, np.ma.MaskedArray)
|
||
|
|
|
||
|
|
def test_method(self):
|
||
|
|
return self._test_base(np.ma.MaskedArray.argsort, np.ma.MaskedArray)
|
||
|
|
|
||
|
|
|
||
|
|
class TestMinimumMaximum:
|
||
|
|
def test_minimum(self):
|
||
|
|
assert_warns(DeprecationWarning, np.ma.minimum, np.ma.array([1, 2]))
|
||
|
|
|
||
|
|
def test_maximum(self):
|
||
|
|
assert_warns(DeprecationWarning, np.ma.maximum, np.ma.array([1, 2]))
|
||
|
|
|
||
|
|
def test_axis_default(self):
|
||
|
|
# NumPy 1.13, 2017-05-06
|
||
|
|
|
||
|
|
data1d = np.ma.arange(6)
|
||
|
|
data2d = data1d.reshape(2, 3)
|
||
|
|
|
||
|
|
ma_min = np.ma.minimum.reduce
|
||
|
|
ma_max = np.ma.maximum.reduce
|
||
|
|
|
||
|
|
# check that the default axis is still None, but warns on 2d arrays
|
||
|
|
result = assert_warns(MaskedArrayFutureWarning, ma_max, data2d)
|
||
|
|
assert_equal(result, ma_max(data2d, axis=None))
|
||
|
|
|
||
|
|
result = assert_warns(MaskedArrayFutureWarning, ma_min, data2d)
|
||
|
|
assert_equal(result, ma_min(data2d, axis=None))
|
||
|
|
|
||
|
|
# no warnings on 1d, as both new and old defaults are equivalent
|
||
|
|
result = ma_min(data1d)
|
||
|
|
assert_equal(result, ma_min(data1d, axis=None))
|
||
|
|
assert_equal(result, ma_min(data1d, axis=0))
|
||
|
|
|
||
|
|
result = ma_max(data1d)
|
||
|
|
assert_equal(result, ma_max(data1d, axis=None))
|
||
|
|
assert_equal(result, ma_max(data1d, axis=0))
|